The following functions configure the logging module. They are located in the
logging.config module. Their use is optional — you can configure the
logging module using these functions or by making calls to the main API (defined
in logging itself) and defining handlers which are declared either in
logging or logging.handlers.

If an error is encountered during configuration, this function will
raise a ValueError, TypeError, AttributeError
or ImportError with a suitably descriptive message. The
following is a (possibly incomplete) list of conditions which will
raise an error:

A level which is not a string or which is a string not
corresponding to an actual logging level.

A propagate value which is not a boolean.

An id which does not have a corresponding destination.

A non-existent handler id found during an incremental call.

An invalid logger name.

Inability to resolve to an internal or external object.

Parsing is performed by the DictConfigurator class, whose
constructor is passed the dictionary used for configuration, and
has a configure() method. The logging.config module
has a callable attribute dictConfigClass
which is initially set to DictConfigurator.
You can replace the value of dictConfigClass with a
suitable implementation of your own.

dictConfig() calls dictConfigClass passing
the specified dictionary, and then calls the configure() method on
the returned object to put the configuration into effect:

defdictConfig(config):dictConfigClass(config).configure()

For example, a subclass of DictConfigurator could call
DictConfigurator.__init__() in its own __init__(), then
set up custom prefixes which would be usable in the subsequent
configure() call. dictConfigClass would be bound to
this new subclass, and then dictConfig() could be called exactly as
in the default, uncustomized state.

Reads the logging configuration from a configparser-format file
named fname. This function can be called several times from an
application, allowing an end user to select from various pre-canned
configurations (if the developer provides a mechanism to present the choices
and load the chosen configuration).

Parameters:

defaults – Defaults to be passed to the ConfigParser can be specified
in this argument.

disable_existing_loggers – If specified as False, loggers which
exist when this call is made are left
alone. The default is True because this
enables old behaviour in a backward-
compatible way. This behaviour is to
disable any existing loggers unless they or
their ancestors are explicitly named in the
logging configuration.

Changed in version 2.6: The disable_existing_loggers keyword argument was added. Previously,
existing loggers were always disabled.

Starts up a socket server on the specified port, and listens for new
configurations. If no port is specified, the module’s default
DEFAULT_LOGGING_CONFIG_PORT is used. Logging configurations will be
sent as a file suitable for processing by fileConfig(). Returns a
Thread instance on which you can call
start() to start the server, and which you can
join() when appropriate. To stop the server,
call stopListening().

To send a configuration to the socket, read in the configuration file and
send it to the socket as a string of bytes preceded by a four-byte length
string packed in binary using struct.pack('>L',n).

Note

Because portions of the configuration are passed through
eval(), use of this function may open its users to a security risk.
While the function only binds to a socket on localhost, and so does
not accept connections from remote machines, there are scenarios where
untrusted code could be run under the account of the process which calls
listen(). Specifically, if the process calling listen() runs
on a multi-user machine where users cannot trust each other, then a
malicious user could arrange to run essentially arbitrary code in a
victim user’s process, simply by connecting to the victim’s
listen() socket and sending a configuration which runs whatever
code the attacker wants to have executed in the victim’s process. This is
especially easy to do if the default port is used, but not hard even if a
different port is used).

Describing a logging configuration requires listing the various
objects to create and the connections between them; for example, you
may create a handler named ‘console’ and then say that the logger
named ‘startup’ will send its messages to the ‘console’ handler.
These objects aren’t limited to those provided by the logging
module because you might write your own formatter or handler class.
The parameters to these classes may also need to include external
objects such as sys.stderr. The syntax for describing these
objects and connections is defined in Object connections
below.

The dictionary passed to dictConfig() must contain the following
keys:

version - to be set to an integer value representing the schema
version. The only valid value at present is 1, but having this key
allows the schema to evolve while still preserving backwards
compatibility.

All other keys are optional, but if present they will be interpreted
as described below. In all cases below where a ‘configuring dict’ is
mentioned, it will be checked for the special '()' key to see if a
custom instantiation is required. If so, the mechanism described in
User-defined objects below is used to create an instance;
otherwise, the context is used to determine what to instantiate.

formatters - the corresponding value will be a dict in which each
key is a formatter id and each value is a dict describing how to
configure the corresponding Formatter instance.

The configuring dict is searched for keys format and datefmt
(with defaults of None) and these are used to construct a
Formatter instance.

filters - the corresponding value will be a dict in which each key
is a filter id and each value is a dict describing how to configure
the corresponding Filter instance.

The configuring dict is searched for the key name (defaulting to the
empty string) and this is used to construct a logging.Filter
instance.

handlers - the corresponding value will be a dict in which each
key is a handler id and each value is a dict describing how to
configure the corresponding Handler instance.

The configuring dict is searched for the following keys:

class (mandatory). This is the fully qualified name of the
handler class.

level (optional). The level of the handler.

formatter (optional). The id of the formatter for this
handler.

filters (optional). A list of ids of the filters for this
handler.

All other keys are passed through as keyword arguments to the
handler’s constructor. For example, given the snippet:

loggers - the corresponding value will be a dict in which each key
is a logger name and each value is a dict describing how to
configure the corresponding Logger instance.

The configuring dict is searched for the following keys:

level (optional). The level of the logger.

propagate (optional). The propagation setting of the logger.

filters (optional). A list of ids of the filters for this
logger.

handlers (optional). A list of ids of the handlers for this
logger.

The specified loggers will be configured according to the level,
propagation, filters and handlers specified.

root - this will be the configuration for the root logger.
Processing of the configuration will be as for any logger, except
that the propagate setting will not be applicable.

incremental - whether the configuration is to be interpreted as
incremental to the existing configuration. This value defaults to
False, which means that the specified configuration replaces the
existing configuration with the same semantics as used by the
existing fileConfig() API.

disable_existing_loggers - whether any existing loggers are to be
disabled. This setting mirrors the parameter of the same name in
fileConfig(). If absent, this parameter defaults to True.
This value is ignored if incremental is True.

It is difficult to provide complete flexibility for incremental
configuration. For example, because objects such as filters
and formatters are anonymous, once a configuration is set up, it is
not possible to refer to such anonymous objects when augmenting a
configuration.

Furthermore, there is not a compelling case for arbitrarily altering
the object graph of loggers, handlers, filters, formatters at
run-time, once a configuration is set up; the verbosity of loggers and
handlers can be controlled just by setting levels (and, in the case of
loggers, propagation flags). Changing the object graph arbitrarily in
a safe way is problematic in a multi-threaded environment; while not
impossible, the benefits are not worth the complexity it adds to the
implementation.

Thus, when the incremental key of a configuration dict is present
and is True, the system will completely ignore any formatters and
filters entries, and process only the level
settings in the handlers entries, and the level and
propagate settings in the loggers and root entries.

Using a value in the configuration dict lets configurations to be sent
over the wire as pickled dicts to a socket listener. Thus, the logging
verbosity of a long-running application can be altered over time with
no need to stop and restart the application.

The schema describes a set of logging objects - loggers,
handlers, formatters, filters - which are connected to each other in
an object graph. Thus, the schema needs to represent connections
between the objects. For example, say that, once configured, a
particular logger has attached to it a particular handler. For the
purposes of this discussion, we can say that the logger represents the
source, and the handler the destination, of a connection between the
two. Of course in the configured objects this is represented by the
logger holding a reference to the handler. In the configuration dict,
this is done by giving each destination object an id which identifies
it unambiguously, and then using the id in the source object’s
configuration to indicate that a connection exists between the source
and the destination object with that id.

(Note: YAML used here because it’s a little more readable than the
equivalent Python source form for the dictionary.)

The ids for loggers are the logger names which would be used
programmatically to obtain a reference to those loggers, e.g.
foo.bar.baz. The ids for Formatters and Filters can be any string
value (such as brief, precise above) and they are transient,
in that they are only meaningful for processing the configuration
dictionary and used to determine connections between objects, and are
not persisted anywhere when the configuration call is complete.

The above snippet indicates that logger named foo.bar.baz should
have two handlers attached to it, which are described by the handler
ids h1 and h2. The formatter for h1 is that described by id
brief, and the formatter for h2 is that described by id
precise.

The schema supports user-defined objects for handlers, filters and
formatters. (Loggers do not need to have different types for
different instances, so there is no support in this configuration
schema for user-defined logger classes.)

Objects to be configured are described by dictionaries
which detail their configuration. In some places, the logging system
will be able to infer from the context how an object is to be
instantiated, but when a user-defined object is to be instantiated,
the system will not know how to do this. In order to provide complete
flexibility for user-defined object instantiation, the user needs
to provide a ‘factory’ - a callable which is called with a
configuration dictionary and which returns the instantiated object.
This is signalled by an absolute import path to the factory being
made available under the special key '()'. Here’s a concrete
example:

The above YAML snippet defines three formatters. The first, with id
brief, is a standard logging.Formatter instance with the
specified format string. The second, with id default, has a
longer format and also defines the time format explicitly, and will
result in a logging.Formatter initialized with those two format
strings. Shown in Python source form, the brief and default
formatters have configuration sub-dictionaries:

respectively, and as these dictionaries do not contain the special key
'()', the instantiation is inferred from the context: as a result,
standard logging.Formatter instances are created. The
configuration sub-dictionary for the third formatter, with id
custom, is:

and this contains the special key '()', which means that
user-defined instantiation is wanted. In this case, the specified
factory callable will be used. If it is an actual callable it will be
used directly - otherwise, if you specify a string (as in the example)
the actual callable will be located using normal import mechanisms.
The callable will be called with the remaining items in the
configuration sub-dictionary as keyword arguments. In the above
example, the formatter with id custom will be assumed to be
returned by the call:

my.package.customFormatterFactory(bar='baz',spam=99.9,answer=42)

The key '()' has been used as the special key because it is not a
valid keyword parameter name, and so will not clash with the names of
the keyword arguments used in the call. The '()' also serves as a
mnemonic that the corresponding value is a callable.

There are times where a configuration needs to refer to objects
external to the configuration, for example sys.stderr. If the
configuration dict is constructed using Python code, this is
straightforward, but a problem arises when the configuration is
provided via a text file (e.g. JSON, YAML). In a text file, there is
no standard way to distinguish sys.stderr from the literal string
'sys.stderr'. To facilitate this distinction, the configuration
system looks for certain special prefixes in string values and
treat them specially. For example, if the literal string
'ext://sys.stderr' is provided as a value in the configuration,
then the ext:// will be stripped off and the remainder of the
value processed using normal import mechanisms.

The handling of such prefixes is done in a way analogous to protocol
handling: there is a generic mechanism to look for prefixes which
match the regular expression ^(?P<prefix>[a-z]+)://(?P<suffix>.*)$
whereby, if the prefix is recognised, the suffix is processed
in a prefix-dependent manner and the result of the processing replaces
the string value. If the prefix is not recognised, then the string
value will be left as-is.

As well as external objects, there is sometimes also a need to refer
to objects in the configuration. This will be done implicitly by the
configuration system for things that it knows about. For example, the
string value 'DEBUG' for a level in a logger or handler will
automatically be converted to the value logging.DEBUG, and the
handlers, filters and formatter entries will take an
object id and resolve to the appropriate destination object.

However, a more generic mechanism is needed for user-defined
objects which are not known to the logging module. For
example, consider logging.handlers.MemoryHandler, which takes
a target argument which is another handler to delegate to. Since
the system already knows about this class, then in the configuration,
the given target just needs to be the object id of the relevant
target handler, and the system will resolve to the handler from the
id. If, however, a user defines a my.package.MyHandler which has
an alternate handler, the configuration system would not know that
the alternate referred to a handler. To cater for this, a generic
resolution system allows the user to specify:

The literal string 'cfg://handlers.file' will be resolved in an
analogous way to strings with the ext:// prefix, but looking
in the configuration itself rather than the import namespace. The
mechanism allows access by dot or by index, in a similar way to
that provided by str.format. Thus, given the following snippet:

in the configuration, the string 'cfg://handlers' would resolve to
the dict with key handlers, the string 'cfg://handlers.email
would resolve to the dict with key email in the handlers dict,
and so on. The string 'cfg://handlers.email.toaddrs[1] would
resolve to 'dev_team.domain.tld' and the string
'cfg://handlers.email.toaddrs[0]' would resolve to the value
'support_team@domain.tld'. The subject value could be accessed
using either 'cfg://handlers.email.subject' or, equivalently,
'cfg://handlers.email[subject]'. The latter form only needs to be
used if the key contains spaces or non-alphanumeric characters. If an
index value consists only of decimal digits, access will be attempted
using the corresponding integer value, falling back to the string
value if needed.

Given a string cfg://handlers.myhandler.mykey.123, this will
resolve to config_dict['handlers']['myhandler']['mykey']['123'].
If the string is specified as cfg://handlers.myhandler.mykey[123],
the system will attempt to retrieve the value from
config_dict['handlers']['myhandler']['mykey'][123], and fall back
to config_dict['handlers']['myhandler']['mykey']['123'] if that
fails.

Import resolution, by default, uses the builtin __import__() function
to do its importing. You may want to replace this with your own importing
mechanism: if so, you can replace the importer attribute of the
DictConfigurator or its superclass, the
BaseConfigurator class. However, you need to be
careful because of the way functions are accessed from classes via
descriptors. If you are using a Python callable to do your imports, and you
want to define it at class level rather than instance level, you need to wrap
it with staticmethod(). For example:

The configuration file format understood by fileConfig() is based on
configparser functionality. The file must contain sections called
[loggers], [handlers] and [formatters] which identify by name the
entities of each type which are defined in the file. For each such entity, there
is a separate section which identifies how that entity is configured. Thus, for
a logger named log01 in the [loggers] section, the relevant
configuration details are held in a section [logger_log01]. Similarly, a
handler called hand01 in the [handlers] section will have its
configuration held in a section called [handler_hand01], while a formatter
called form01 in the [formatters] section will have its configuration
specified in a section called [formatter_form01]. The root logger
configuration must be specified in a section called [logger_root].

The root logger must specify a level and a list of handlers. An example of a
root logger section is given below.

[logger_root]level=NOTSEThandlers=hand01

The level entry can be one of DEBUG,INFO,WARNING,ERROR,CRITICAL or
NOTSET. For the root logger only, NOTSET means that all messages will be
logged. Level values are eval()uated in the context of the logging
package’s namespace.

The handlers entry is a comma-separated list of handler names, which must
appear in the [handlers] section. These names must appear in the
[handlers] section and have corresponding sections in the configuration
file.

For loggers other than the root logger, some additional information is required.
This is illustrated by the following example.

The level and handlers entries are interpreted as for the root logger,
except that if a non-root logger’s level is specified as NOTSET, the system
consults loggers higher up the hierarchy to determine the effective level of the
logger. The propagate entry is set to 1 to indicate that messages must
propagate to handlers higher up the logger hierarchy from this logger, or 0 to
indicate that messages are not propagated to handlers up the hierarchy. The
qualname entry is the hierarchical channel name of the logger, that is to
say the name used by the application to get the logger.

Sections which specify handler configuration are exemplified by the following.

The class entry indicates the handler’s class (as determined by eval()
in the logging package’s namespace). The level is interpreted as for
loggers, and NOTSET is taken to mean ‘log everything’.

Changed in version 2.6: Added support for resolving the handler’s class as a dotted module and
class name.

The formatter entry indicates the key name of the formatter for this
handler. If blank, a default formatter (logging._defaultFormatter) is used.
If a name is specified, it must appear in the [formatters] section and have
a corresponding section in the configuration file.

The args entry, when eval()uated in the context of the logging
package’s namespace, is the list of arguments to the constructor for the handler
class. Refer to the constructors for the relevant handlers, or to the examples
below, to see how typical entries are constructed.

The format entry is the overall format string, and the datefmt entry is
the strftime()-compatible date/time format string. If empty, the
package substitutes ISO8601 format date/times, which is almost equivalent to
specifying the date format string '%Y-%m-%d%H:%M:%S'. The ISO8601 format
also specifies milliseconds, which are appended to the result of using the above
format string, with a comma separator. An example time in ISO8601 format is
2003-01-2300:29:50,411.

The class entry is optional. It indicates the name of the formatter’s class
(as a dotted module and class name.) This option is useful for instantiating a
Formatter subclass. Subclasses of
Formatter can present exception tracebacks in an expanded or
condensed format.

Note

Due to the use of eval() as described above, there are
potential security risks which result from using the listen() to send
and receive configurations via sockets. The risks are limited to where
multiple users with no mutual trust run code on the same machine; see the
listen() documentation for more information.